Relational Analysis and Precision via Probabilistic Abstract Interpretation

Within the context of a quantitative generalisation of the well established framework of Abstract Interpretation – i.e. Probabilistic Abstract Interpretation – we investigate a quantitative notion of precision which allows us to compare analyses on the basis of their expected exactness for a given program. We illustrate this approach by considering various types of numerical abstractions of the values of variables for independent analysis as well as weakly and fully relational analysis. We utilise for this a linear operator semantics of a simple imperative programming language. In this setting, fully relational dependencies are realised via the tensor product. Independent analyses and weakly relational analyses are realised as abstractions of the fully relational analysis.     

A simple method for improving local binary patterns by considering non-uniform patterns

The basic idea behind LBP is that an image is composed of micropatterns. A histogram of these micropatterns contains information about the local features in an image. These micropatterns can be divided into two types: uniform and non-uniform. In standard applications using LBP, only the uniform patterns are used. The non-uniform patterns are considered in only a single bin of the histogram that is used to extract features in the classification stage. Non-uniform patterns have undesirable characteristics: they are of a high dimension, partially correlated, and introduce unwanted noise. To offset these disadvantages, we explore using random subspace, well-known to work well with noise and correlated features, to train features based also on non-uniform patterns. We find that a stand-alone support vector machine performs best with the uniform patterns and random subspace with histograms of 50 bins performs best with the non-uniform patterns. Superior results are obtained when the two are combined. Based on extensive experiments conducted in several domains using several benchmark databases, it is our conclusion that non-uniform patterns improve classifier performance.     

Self-Organising Maps: A new way to screen the level of satisfaction of dialysis patients

Evaluation of patient satisfaction has become an important indicator for assessing health care quality. Fresenius Medical Care (FME) as a global provider of dialysis services through its NephroCare network has a strong interest in monitoring patient satisfaction.The aim of the paper is to test and validate a methodology for detecting a residual area of low satisfaction in dialysis patients.The FME Patient Satisfaction Programme questionnaire was distributed to haemodialysis (HD) patients treated in 335 centers of its network. It contained 79 questions covering various satisfaction aspects regarding Dialysis Unit, Dialysis Arrangement, Nurses, Doctors, etc.To analyse the data provided by the questionnaire, the Self-Organising Map (SOM) method was used. SOM is a neural network model for clustering and projecting high-dimensional data into a low-dimensional space, preserving topological relationships of original high-dimensional data spaces.10,632 HD patients completed the questionnaire. Mean age was 63.05 ± 14.93 years with 56.69% males. Response rate was 66%. Overall level of satisfaction was 1.99 (range from ?3 to +3). On average patients were very satisfied with all issues. Nevertheless, a group of patients, around 60 years old, balanced gender ratio, whose level of satisfaction was lower than 1, were highlighted.In the NephroCare clinics patient satisfaction with service is rather high. While traditional analysis usually stops here, the SOM method allows identification of areas of potential improvement for specific patient groups.     

Clotrimazole-loaded Eudragit® RS100 nanocapsules: Preparation,characterization and in vitro evaluation of antifungal activity against Candida species

Clotrimazole is a common choice for the treatment of vulvovaginal infections, but its low solubility and some side effects pose a challenge to its application. This work evaluated the feasibility to formulate clotrimazole-loaded cationic nanocapsules using Eudragit® RS100 and medium chain triglycerides as polymer and oily core, respectively, by the method of interfacial deposition of a preformed polymer. The physicochemical characteristics of nanocapsule formulations were evaluated at 0 day and 60 days after preparation. Particle size, zeta potential, polydispersity index, pH and drug content were stable during this period. In addition, nanocapsules were able to protect clotrimazole from photodegradation under UV radiation. By the dialysis bag diffusion technique, the nanosized formulations showed prolonged release of clotrimazole by anomalous transport and first order kinetics. A microbiological study was carried out by the microdilution method and showed that nanocapsules (mean size: 144 nm; zeta potential: + 12 mV) maintained the antifungal activity of clotrimazole against Candida albicans and Candida glabrata strains susceptible and resistant to fluconazole.     

Effect of Change in Strain Path During Cold Rolling on the Evolution of Microstructure and Texture in Al and Al-2.5%Mg

The effect of change in strain path during cold rolling on the evolution of microstructure and texture is investigated. For this purpose, high purity aluminum and Al-2.5%Mg alloy are deformed (~90% reduction in thickness) by unidirectional and cross cold rolling. Irrespective of the alloy system, copper-type texture is observed in unidirectional processed materials, while strong brass ({011}〈112〉) texture is developed during cross rolling. Unidirectionally rolled aluminum showed higher HAGB fraction, but similar HAGB spacing as compared to the cross-rolled aluminum after 90% reduction in thickness. At the same time, the internal misorientation in the cross-rolled 2N-Al is higher than in the unidirectionally rolled material. In contrast, Al-2.5% Mg alloy processed differently in both ways shows similar HAGB fraction, spacing, and internal misorientation distribution. These observations indicate that microstructure evolution due to strain path change is more strongly affected by dynamic recovery as compared to texture evolution.